{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Discretization\n",
    "\n",
    "---\n",
    "\n",
    "In this notebook, you will deal with continuous state and action spaces by discretizing them. This will enable you to apply reinforcement learning algorithms that are only designed to work with discrete spaces.\n",
    "\n",
    "### 1. Import the Necessary Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import gym\n",
    "import numpy as np\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Set plotting options\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot')\n",
    "np.set_printoptions(precision=3, linewidth=120)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Specify the Environment, and Explore the State and Action Spaces\n",
    "\n",
    "We'll use [OpenAI Gym](https://gym.openai.com/) environments to test and develop our algorithms. These simulate a variety of classic as well as contemporary reinforcement learning tasks.  Let's use an environment that has a continuous state space, but a discrete action space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# Create an environment and set random seed\n",
    "env = gym.make('MountainCar-v0')\n",
    "env.seed(505);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the next code cell to watch a random agent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final score: -200.0\n"
     ]
    }
   ],
   "source": [
    "state = env.reset()\n",
    "score = 0\n",
    "for t in range(200):\n",
    "    action = env.action_space.sample()\n",
    "    env.render()\n",
    "    state, reward, done, _ = env.step(action)\n",
    "    score += reward\n",
    "    if done:\n",
    "        break \n",
    "print('Final score:', score)\n",
    "env.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, you will train an agent to perform much better!  For now, we can explore the state and action spaces, as well as sample them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "State space: Box(2,)\n",
      "- low: [-1.2  -0.07]\n",
      "- high: [0.6  0.07]\n"
     ]
    }
   ],
   "source": [
    "# Explore state (observation) space\n",
    "print(\"State space:\", env.observation_space)\n",
    "print(\"- low:\", env.observation_space.low)\n",
    "print(\"- high:\", env.observation_space.high)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "State space samples:\n",
      "[[-0.622 -0.039]\n",
      " [-0.946 -0.056]\n",
      " [ 0.571 -0.034]\n",
      " [-0.233 -0.007]\n",
      " [-1.021 -0.021]\n",
      " [-0.355  0.048]\n",
      " [ 0.428 -0.065]\n",
      " [-0.285 -0.047]\n",
      " [ 0.202  0.051]\n",
      " [-0.459 -0.05 ]]\n"
     ]
    }
   ],
   "source": [
    "# Generate some samples from the state space \n",
    "print(\"State space samples:\")\n",
    "print(np.array([env.observation_space.sample() for i in range(10)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Action space: Discrete(3)\n",
      "Action space samples:\n",
      "[1 1 1 2 2 2 0 1 2 1]\n"
     ]
    }
   ],
   "source": [
    "# Explore the action space\n",
    "print(\"Action space:\", env.action_space)\n",
    "\n",
    "# Generate some samples from the action space\n",
    "print(\"Action space samples:\")\n",
    "print(np.array([env.action_space.sample() for i in range(10)]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Discretize the State Space with a Uniform Grid\n",
    "\n",
    "We will discretize the space using a uniformly-spaced grid. Implement the following function to create such a grid, given the lower bounds (`low`), upper bounds (`high`), and number of desired `bins` along each dimension. It should return the split points for each dimension, which will be 1 less than the number of bins.\n",
    "\n",
    "For instance, if `low = [-1.0, -5.0]`, `high = [1.0, 5.0]`, and `bins = (10, 10)`, then your function should return the following list of 2 NumPy arrays:\n",
    "\n",
    "```\n",
    "[array([-0.8, -0.6, -0.4, -0.2,  0.0,  0.2,  0.4,  0.6,  0.8]),\n",
    " array([-4.0, -3.0, -2.0, -1.0,  0.0,  1.0,  2.0,  3.0,  4.0])]\n",
    "```\n",
    "\n",
    "Note that the ends of `low` and `high` are **not** included in these split points. It is assumed that any value below the lowest split point maps to index `0` and any value above the highest split point maps to index `n-1`, where `n` is the number of bins along that dimension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uniform grid: [<low>, <high>] / <bins> => <splits>\n",
      "    [-1.0, 1.0] / 10 => [-0.8 -0.6 -0.4 -0.2  0.   0.2  0.4  0.6  0.8]\n",
      "    [-5.0, 5.0] / 10 => [-4. -3. -2. -1.  0.  1.  2.  3.  4.]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[array([-0.8, -0.6, -0.4, -0.2,  0. ,  0.2,  0.4,  0.6,  0.8]),\n",
       " array([-4., -3., -2., -1.,  0.,  1.,  2.,  3.,  4.])]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def create_uniform_grid(low, high, bins=(10, 10)):\n",
    "    \"\"\"Define a uniformly-spaced grid that can be used to discretize a space.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    low : array_like\n",
    "        Lower bounds for each dimension of the continuous space.\n",
    "    high : array_like\n",
    "        Upper bounds for each dimension of the continuous space.\n",
    "    bins : tuple\n",
    "        Number of bins along each corresponding dimension.\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    grid : list of array_like\n",
    "        A list of arrays containing split points for each dimension.\n",
    "    \"\"\"\n",
    "    # TODO: Implement this\n",
    "    grid = [np.linspace(low[dim], high[dim], bins[dim] + 1)[1:-1] for dim in range(len(bins))]\n",
    "    print(\"Uniform grid: [<low>, <high>] / <bins> => <splits>\")\n",
    "    for l, h, b, splits in zip(low, high, bins, grid):\n",
    "        print(\"    [{}, {}] / {} => {}\".format(l, h, b, splits))\n",
    "    return grid\n",
    "\n",
    "\n",
    "low = [-1.0, -5.0]\n",
    "high = [1.0, 5.0]\n",
    "create_uniform_grid(low, high)  # [test]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now write a function that can convert samples from a continuous space into its equivalent discretized representation, given a grid like the one you created above. You can use the [`numpy.digitize()`](https://docs.scipy.org/doc/numpy-1.9.3/reference/generated/numpy.digitize.html) function for this purpose.\n",
    "\n",
    "Assume the grid is a list of NumPy arrays containing the following split points:\n",
    "```\n",
    "[array([-0.8, -0.6, -0.4, -0.2,  0.0,  0.2,  0.4,  0.6,  0.8]),\n",
    " array([-4.0, -3.0, -2.0, -1.0,  0.0,  1.0,  2.0,  3.0,  4.0])]\n",
    "```\n",
    "\n",
    "Here are some potential samples and their corresponding discretized representations:\n",
    "```\n",
    "[-1.0 , -5.0] => [0, 0]\n",
    "[-0.81, -4.1] => [0, 0]\n",
    "[-0.8 , -4.0] => [1, 1]\n",
    "[-0.5 ,  0.0] => [2, 5]\n",
    "[ 0.2 , -1.9] => [6, 3]\n",
    "[ 0.8 ,  4.0] => [9, 9]\n",
    "[ 0.81,  4.1] => [9, 9]\n",
    "[ 1.0 ,  5.0] => [9, 9]\n",
    "```\n",
    "\n",
    "**Note**: There may be one-off differences in binning due to floating-point inaccuracies when samples are close to grid boundaries, but that is alright."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uniform grid: [<low>, <high>] / <bins> => <splits>\n",
      "    [-1.0, 1.0] / 10 => [-0.8 -0.6 -0.4 -0.2  0.   0.2  0.4  0.6  0.8]\n",
      "    [-5.0, 5.0] / 10 => [-4. -3. -2. -1.  0.  1.  2.  3.  4.]\n",
      "\n",
      "Samples:\n",
      "array([[-1.  , -5.  ],\n",
      "       [-0.81, -4.1 ],\n",
      "       [-0.8 , -4.  ],\n",
      "       [-0.5 ,  0.  ],\n",
      "       [ 0.2 , -1.9 ],\n",
      "       [ 0.8 ,  4.  ],\n",
      "       [ 0.81,  4.1 ],\n",
      "       [ 1.  ,  5.  ]])\n",
      "\n",
      "Discretized samples:\n",
      "array([[0, 0],\n",
      "       [0, 0],\n",
      "       [1, 1],\n",
      "       [2, 5],\n",
      "       [5, 3],\n",
      "       [9, 9],\n",
      "       [9, 9],\n",
      "       [9, 9]])\n"
     ]
    }
   ],
   "source": [
    "def discretize(sample, grid):\n",
    "    \"\"\"Discretize a sample as per given grid.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    sample : array_like\n",
    "        A single sample from the (original) continuous space.\n",
    "    grid : list of array_like\n",
    "        A list of arrays containing split points for each dimension.\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    discretized_sample : array_like\n",
    "        A sequence of integers with the same number of dimensions as sample.\n",
    "    \"\"\"\n",
    "    # TODO: Implement this\n",
    "    return list(int(np.digitize(s, g)) for s, g in zip(sample, grid))  # apply along each dimension\n",
    "\n",
    "\n",
    "# Test with a simple grid and some samples\n",
    "grid = create_uniform_grid([-1.0, -5.0], [1.0, 5.0])\n",
    "samples = np.array(\n",
    "    [[-1.0 , -5.0],\n",
    "     [-0.81, -4.1],\n",
    "     [-0.8 , -4.0],\n",
    "     [-0.5 ,  0.0],\n",
    "     [ 0.2 , -1.9],\n",
    "     [ 0.8 ,  4.0],\n",
    "     [ 0.81,  4.1],\n",
    "     [ 1.0 ,  5.0]])\n",
    "discretized_samples = np.array([discretize(sample, grid) for sample in samples])\n",
    "print(\"\\nSamples:\", repr(samples), sep=\"\\n\")\n",
    "print(\"\\nDiscretized samples:\", repr(discretized_samples), sep=\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Visualization\n",
    "\n",
    "It might be helpful to visualize the original and discretized samples to get a sense of how much error you are introducing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ca25b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.collections as mc\n",
    "\n",
    "def visualize_samples(samples, discretized_samples, grid, low=None, high=None):\n",
    "    \"\"\"Visualize original and discretized samples on a given 2-dimensional grid.\"\"\"\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(10, 10))\n",
    "    \n",
    "    # Show grid\n",
    "    ax.xaxis.set_major_locator(plt.FixedLocator(grid[0]))\n",
    "    ax.yaxis.set_major_locator(plt.FixedLocator(grid[1]))\n",
    "    ax.grid(True)\n",
    "    \n",
    "    # If bounds (low, high) are specified, use them to set axis limits\n",
    "    if low is not None and high is not None:\n",
    "        ax.set_xlim(low[0], high[0])\n",
    "        ax.set_ylim(low[1], high[1])\n",
    "    else:\n",
    "        # Otherwise use first, last grid locations as low, high (for further mapping discretized samples)\n",
    "        low = [splits[0] for splits in grid]\n",
    "        high = [splits[-1] for splits in grid]\n",
    "\n",
    "    # Map each discretized sample (which is really an index) to the center of corresponding grid cell\n",
    "    grid_extended = np.hstack((np.array([low]).T, grid, np.array([high]).T))  # add low and high ends\n",
    "    grid_centers = (grid_extended[:, 1:] + grid_extended[:, :-1]) / 2  # compute center of each grid cell\n",
    "    locs = np.stack(grid_centers[i, discretized_samples[:, i]] for i in range(len(grid))).T  # map discretized samples\n",
    "\n",
    "    ax.plot(samples[:, 0], samples[:, 1], 'o')  # plot original samples\n",
    "    ax.plot(locs[:, 0], locs[:, 1], 's')  # plot discretized samples in mapped locations\n",
    "    ax.add_collection(mc.LineCollection(list(zip(samples, locs)), colors='orange'))  # add a line connecting each original-discretized sample\n",
    "    ax.legend(['original', 'discretized'])\n",
    "\n",
    "    \n",
    "visualize_samples(samples, discretized_samples, grid, low, high)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have a way to discretize a state space, let's apply it to our reinforcement learning environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uniform grid: [<low>, <high>] / <bins> => <splits>\n",
      "    [-1.2000000476837158, 0.6000000238418579] / 10 => [-1.02 -0.84 -0.66 -0.48 -0.3  -0.12  0.06  0.24  0.42]\n",
      "    [-0.07000000029802322, 0.07000000029802322] / 10 => [-0.056 -0.042 -0.028 -0.014  0.     0.014  0.028  0.042  0.056]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[array([-1.02, -0.84, -0.66, -0.48, -0.3 , -0.12,  0.06,  0.24,  0.42]),\n",
       " array([-0.056, -0.042, -0.028, -0.014,  0.   ,  0.014,  0.028,  0.042,  0.056])]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a grid to discretize the state space\n",
    "state_grid = create_uniform_grid(env.observation_space.low, env.observation_space.high, bins=(10, 10))\n",
    "state_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c834b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Obtain some samples from the space, discretize them, and then visualize them\n",
    "state_samples = np.array([env.observation_space.sample() for i in range(10)])\n",
    "discretized_state_samples = np.array([discretize(sample, state_grid) for sample in state_samples])\n",
    "visualize_samples(state_samples, discretized_state_samples, state_grid,\n",
    "                  env.observation_space.low, env.observation_space.high)\n",
    "plt.xlabel('position'); plt.ylabel('velocity');  # axis labels for MountainCar-v0 state space"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You might notice that if you have enough bins, the discretization doesn't introduce too much error into your representation.  So we may be able to now apply a reinforcement learning algorithm (like Q-Learning) that operates on discrete spaces.  Give it a shot to see how well it works!\n",
    "\n",
    "### 5. Q-Learning\n",
    "\n",
    "Provided below is a simple Q-Learning agent. Implement the `preprocess_state()` method to convert each continuous state sample to its corresponding discretized representation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Environment: <TimeLimit<MountainCarEnv<MountainCar-v0>>>\n",
      "State space size: (10, 10)\n",
      "Action space size: 3\n",
      "Q table size: (10, 10, 3)\n"
     ]
    }
   ],
   "source": [
    "class QLearningAgent:\n",
    "    \"\"\"Q-Learning agent that can act on a continuous state space by discretizing it.\"\"\"\n",
    "\n",
    "    def __init__(self, env, state_grid, alpha=0.02, gamma=0.99,\n",
    "                 epsilon=1.0, epsilon_decay_rate=0.9995, min_epsilon=.01, seed=505):\n",
    "        \"\"\"Initialize variables, create grid for discretization.\"\"\"\n",
    "        # Environment info\n",
    "        self.env = env\n",
    "        self.state_grid = state_grid\n",
    "        self.state_size = tuple(len(splits) + 1 for splits in self.state_grid)  # n-dimensional state space\n",
    "        self.action_size = self.env.action_space.n  # 1-dimensional discrete action space\n",
    "        self.seed = np.random.seed(seed)\n",
    "        print(\"Environment:\", self.env)\n",
    "        print(\"State space size:\", self.state_size)\n",
    "        print(\"Action space size:\", self.action_size)\n",
    "        \n",
    "        # Learning parameters\n",
    "        self.alpha = alpha  # learning rate\n",
    "        self.gamma = gamma  # discount factor\n",
    "        self.epsilon = self.initial_epsilon = epsilon  # initial exploration rate\n",
    "        self.epsilon_decay_rate = epsilon_decay_rate # how quickly should we decrease epsilon\n",
    "        self.min_epsilon = min_epsilon\n",
    "        \n",
    "        # Create Q-table\n",
    "        self.q_table = np.zeros(shape=(self.state_size + (self.action_size,)))\n",
    "        print(\"Q table size:\", self.q_table.shape)\n",
    "\n",
    "    def preprocess_state(self, state):\n",
    "        \"\"\"Map a continuous state to its discretized representation.\"\"\"\n",
    "        # TODO: Implement this\n",
    "        return tuple(discretize(state, self.state_grid))\n",
    "\n",
    "    def reset_episode(self, state):\n",
    "        \"\"\"Reset variables for a new episode.\"\"\"\n",
    "        # Gradually decrease exploration rate\n",
    "        self.epsilon *= self.epsilon_decay_rate\n",
    "        self.epsilon = max(self.epsilon, self.min_epsilon)\n",
    "\n",
    "        # Decide initial action\n",
    "        self.last_state = self.preprocess_state(state)\n",
    "        self.last_action = np.argmax(self.q_table[self.last_state])\n",
    "        return self.last_action\n",
    "    \n",
    "    def reset_exploration(self, epsilon=None):\n",
    "        \"\"\"Reset exploration rate used when training.\"\"\"\n",
    "        self.epsilon = epsilon if epsilon is not None else self.initial_epsilon\n",
    "\n",
    "    def act(self, state, reward=None, done=None, mode='train'):\n",
    "        \"\"\"Pick next action and update internal Q table (when mode != 'test').\"\"\"\n",
    "        state = self.preprocess_state(state)\n",
    "        if mode == 'test':\n",
    "            # Test mode: Simply produce an action\n",
    "            action = np.argmax(self.q_table[state])\n",
    "        else:\n",
    "            # Train mode (default): Update Q table, pick next action\n",
    "            # Note: We update the Q table entry for the *last* (state, action) pair with current state, reward\n",
    "            self.q_table[self.last_state + (self.last_action,)] += self.alpha * \\\n",
    "                (reward + self.gamma * max(self.q_table[state]) - self.q_table[self.last_state + (self.last_action,)])\n",
    "\n",
    "            # Exploration vs. exploitation\n",
    "            do_exploration = np.random.uniform(0, 1) < self.epsilon\n",
    "            if do_exploration:\n",
    "                # Pick a random action\n",
    "                action = np.random.randint(0, self.action_size)\n",
    "            else:\n",
    "                # Pick the best action from Q table\n",
    "                action = np.argmax(self.q_table[state])\n",
    "\n",
    "        # Roll over current state, action for next step\n",
    "        self.last_state = state\n",
    "        self.last_action = action\n",
    "        return action\n",
    "\n",
    "    \n",
    "q_agent = QLearningAgent(env, state_grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also define a convenience function to run an agent on a given environment.  When calling this function, you can pass in `mode='test'` to tell the agent not to learn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Episode 20000/20000 | Max Average Score: -137.36"
     ]
    }
   ],
   "source": [
    "def run(agent, env, num_episodes=20000, mode='train'):\n",
    "    \"\"\"Run agent in given reinforcement learning environment and return scores.\"\"\"\n",
    "    scores = []\n",
    "    max_avg_score = -np.inf\n",
    "    for i_episode in range(1, num_episodes+1):\n",
    "        # Initialize episode\n",
    "        state = env.reset()\n",
    "        action = agent.reset_episode(state)\n",
    "        total_reward = 0\n",
    "        done = False\n",
    "\n",
    "        # Roll out steps until done\n",
    "        while not done:\n",
    "            state, reward, done, info = env.step(action)\n",
    "            total_reward += reward\n",
    "            action = agent.act(state, reward, done, mode)\n",
    "\n",
    "        # Save final score\n",
    "        scores.append(total_reward)\n",
    "        \n",
    "        # Print episode stats\n",
    "        if mode == 'train':\n",
    "            if len(scores) > 100:\n",
    "                avg_score = np.mean(scores[-100:])\n",
    "                if avg_score > max_avg_score:\n",
    "                    max_avg_score = avg_score\n",
    "            if i_episode % 100 == 0:\n",
    "                print(\"\\rEpisode {}/{} | Max Average Score: {}\".format(i_episode, num_episodes, max_avg_score), end=\"\")\n",
    "                sys.stdout.flush()\n",
    "\n",
    "    return scores\n",
    "\n",
    "scores = run(q_agent, env)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best way to analyze if your agent was learning the task is to plot the scores. It should generally increase as the agent goes through more episodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a9c8400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot scores obtained per episode\n",
    "plt.plot(scores); plt.title(\"Scores\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the scores are noisy, it might be difficult to tell whether your agent is actually learning. To find the underlying trend, you may want to plot a rolling mean of the scores. Let's write a convenience function to plot both raw scores as well as a rolling mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a8eec50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_scores(scores, rolling_window=100):\n",
    "    \"\"\"Plot scores and optional rolling mean using specified window.\"\"\"\n",
    "    plt.plot(scores); plt.title(\"Scores\");\n",
    "    rolling_mean = pd.Series(scores).rolling(rolling_window).mean()\n",
    "    plt.plot(rolling_mean);\n",
    "    return rolling_mean\n",
    "\n",
    "rolling_mean = plot_scores(scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should observe the mean episode scores go up over time. Next, you can freeze learning and run the agent in test mode to see how well it performs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[TEST] Completed 100 episodes with avg. score = -176.1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a9b04e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run in test mode and analyze scores obtained\n",
    "test_scores = run(q_agent, env, num_episodes=100, mode='test')\n",
    "print(\"[TEST] Completed {} episodes with avg. score = {}\".format(len(test_scores), np.mean(test_scores)))\n",
    "_ = plot_scores(test_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's also interesting to look at the final Q-table that is learned by the agent. Note that the Q-table is of size MxNxA, where (M, N) is the size of the state space, and A is the size of the action space. We are interested in the maximum Q-value for each state, and the corresponding (best) action associated with that value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11aa89b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_q_table(q_table):\n",
    "    \"\"\"Visualize max Q-value for each state and corresponding action.\"\"\"\n",
    "    q_image = np.max(q_table, axis=2)       # max Q-value for each state\n",
    "    q_actions = np.argmax(q_table, axis=2)  # best action for each state\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(10, 10))\n",
    "    cax = ax.imshow(q_image, cmap='jet');\n",
    "    cbar = fig.colorbar(cax)\n",
    "    for x in range(q_image.shape[0]):\n",
    "        for y in range(q_image.shape[1]):\n",
    "            ax.text(x, y, q_actions[x, y], color='white',\n",
    "                    horizontalalignment='center', verticalalignment='center')\n",
    "    ax.grid(False)\n",
    "    ax.set_title(\"Q-table, size: {}\".format(q_table.shape))\n",
    "    ax.set_xlabel('position')\n",
    "    ax.set_ylabel('velocity')\n",
    "\n",
    "\n",
    "plot_q_table(q_agent.q_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 6. Modify the Grid\n",
    "\n",
    "Now it's your turn to play with the grid definition and see what gives you optimal results. Your agent's final performance is likely to get better if you use a finer grid, with more bins per dimension, at the cost of higher model complexity (more parameters to learn)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uniform grid: [<low>, <high>] / <bins> => <splits>\n",
      "    [-1.2000000476837158, 0.6000000238418579] / 20 => [-1.11 -1.02 -0.93 -0.84 -0.75 -0.66 -0.57 -0.48 -0.39 -0.3  -0.21 -0.12 -0.03  0.06  0.15  0.24  0.33  0.42  0.51]\n",
      "    [-0.07000000029802322, 0.07000000029802322] / 20 => [-0.063 -0.056 -0.049 -0.042 -0.035 -0.028 -0.021 -0.014 -0.007  0.     0.007  0.014  0.021  0.028  0.035  0.042  0.049\n",
      "  0.056  0.063]\n",
      "Environment: <TimeLimit<MountainCarEnv<MountainCar-v0>>>\n",
      "State space size: (20, 20)\n",
      "Action space size: 3\n",
      "Q table size: (20, 20, 3)\n"
     ]
    }
   ],
   "source": [
    "# TODO: Create a new agent with a different state space grid\n",
    "state_grid_new = create_uniform_grid(env.observation_space.low, env.observation_space.high, bins=(20, 20))\n",
    "q_agent_new = QLearningAgent(env, state_grid_new)\n",
    "q_agent_new.scores = []  # initialize a list to store scores for this agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Episode 50000/50000 | Max Average Score: -109.87"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11216ff98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train it over a desired number of episodes and analyze scores\n",
    "# Note: This cell can be run multiple times, and scores will get accumulated\n",
    "q_agent_new.scores += run(q_agent_new, env, num_episodes=50000)  # accumulate scores\n",
    "rolling_mean_new = plot_scores(q_agent_new.scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[TEST] Completed 100 episodes with avg. score = -107.66\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d1a4b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run in test mode and analyze scores obtained\n",
    "test_scores = run(q_agent_new, env, num_episodes=100, mode='test')\n",
    "print(\"[TEST] Completed {} episodes with avg. score = {}\".format(len(test_scores), np.mean(test_scores)))\n",
    "_ = plot_scores(test_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110f04828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the learned Q-table\n",
    "plot_q_table(q_agent_new.q_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Watch a Smart Agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final score: -110.0\n"
     ]
    }
   ],
   "source": [
    "state = env.reset()\n",
    "score = 0\n",
    "for t in range(200):\n",
    "    action = q_agent_new.act(state, mode='test')\n",
    "    env.render()\n",
    "    state, reward, done, _ = env.step(action)\n",
    "    score += reward\n",
    "    if done:\n",
    "        break \n",
    "print('Final score:', score)\n",
    "env.close()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
